Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x29198beeef0>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x29198caa710>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_input')
    z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    return real_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

import collections

Shape = collections.namedtuple('Shape', ('width', 'height', 'depth'))

# Shapes (just for information)
mnist_shape = Shape(28, 28, 1)
celeba_shape = Shape(28, 28, 3)

import functools
import operator

def flatten_size(shape):
    """Calculates flatten size by Shape"""
    return functools.reduce(operator.mul, shape)

def flatten(x, conv_shape):
    """Converts convolutional layer to dense layer"""
    return tf.reshape(x, (-1, flatten_size(conv_shape)))                    

def leaky_relu(x, alpha):
    return tf.maximum(alpha * x, x)

DISCRIMINATOR_VAR_SCOPE = 'discriminator'
                      
def discriminator(images, reuse=False, alpha=0.2, rate=0.2, kernel_size=3):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    
    kernel_initializer = tf.contrib.layers.xavier_initializer_conv2d
    
    with tf.variable_scope(DISCRIMINATOR_VAR_SCOPE, reuse=reuse):
        # Input layer is 28x28x1 or 28x28x3
        
        x1 = tf.layers.conv2d(images, 128, kernel_size, strides=2, padding='same', kernel_initializer=kernel_initializer())
        relu1 = leaky_relu(x1, alpha)
        # dropout1 = tf.layers.dropout(relu1, rate, training=True)
        out1 = relu1
        # 16x16x128 now
                
        x2 = tf.layers.conv2d(out1, 256, kernel_size, strides=2, padding='same', kernel_initializer=kernel_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = leaky_relu(bn2, alpha)
        # dropout2 = tf.layers.dropout(relu2, rate, training=True)
        out2 = relu2
        # 8x8x256 now
        
        x3 = tf.layers.conv2d(out2, 512, kernel_size, strides=2, padding='same', kernel_initializer=kernel_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = leaky_relu(bn3, alpha)
        # dropout3 = tf.layers.dropout(relu3, rate, training=True)
        out3 = relu3
        # 4x4x512 now

        out3_shape = Shape(*[int(dimension) for dimension in out3.shape[1:]])
        
        # Flatten it
        flat = flatten(out3, out3_shape)
        
        logits = tf.layers.dense(flat, 1, kernel_initializer=kernel_initializer())
        
        logits = tf.layers.dropout(logits, rate, training=True)
        
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def start_convolutional(x, shape, kernel_initializer):
    """Starts convolutional stack
    
    shape is a Shape object
    """
    y = tf.layers.dense(x, flatten_size(shape), kernel_initializer=kernel_initializer)
    # Reshape y to start the convolutional stack
    y = tf.reshape(y, (-1, shape.width, shape.height, shape.depth))
    return y

GENERATOR_VAR_SCOPE = 'generator'

def generator(z, out_channel_dim, is_train=True, alpha=0.2, rate=0.2, kernel_size=3):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
    kernel_initializer = tf.contrib.layers.xavier_initializer_conv2d
    
    with tf.variable_scope(GENERATOR_VAR_SCOPE, reuse=not is_train):
        
        x1 = start_convolutional(z, Shape(7, 7, 512), kernel_initializer())
        bn1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = leaky_relu(bn1, alpha)
        # dropout1 = tf.layers.dropout(relu1, rate, training=is_train)
        out1 = relu1
        # 7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(out1, 256, kernel_size, strides=1, padding='same',
                                        kernel_initializer=kernel_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = leaky_relu(bn2, alpha)
        # dropout2 = tf.layers.dropout(relu2, rate, training=is_train)
        out2 = relu2
        # 14x14x256 now
        
        x3 = tf.layers.conv2d_transpose(out2, 128, kernel_size, strides=2, padding='same',
                                        kernel_initializer=kernel_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = leaky_relu(bn3, alpha)
        # dropout3 = tf.layers.dropout(relu3, rate, training=is_train)
        out3 = relu3
        # 28x28x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(out3, out_channel_dim, 5, strides=2, padding='same',
                                            kernel_initializer=kernel_initializer())
        # 28x28x3 now (or 28x28x1)
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim, alpha=0.2,
               label_smoothness=0.1):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_out = generator(input_z, out_channel_dim, is_train=True, alpha=alpha)
    d_real_out, d_real_logits = discriminator(input_real, reuse=False, alpha=alpha)
    d_fake_out, d_fake_logits = discriminator(g_out, reuse=True, alpha=alpha)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_real_logits, 
            labels=tf.ones_like(d_real_out) * (1 - label_smoothness)))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_fake_logits, 
            labels=tf.zeros_like(d_fake_out)))
    
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_fake_logits, 
            labels=tf.ones_like(d_fake_out)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith(DISCRIMINATOR_VAR_SCOPE)]
    g_vars = [var for var in t_vars if var.name.startswith(GENERATOR_VAR_SCOPE)]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, 
          alpha=0.2,
          print_every=10, show_every=50, show_images=25):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    _, image_width, image_height, image_channels = data_shape      
    
    # Build model
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)        
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha=alpha)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    # let's go
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                steps += 1
                
                # images normalization [-1, 1]
                batch_images = batch_images * 2
                
                # white noise for generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                 # Run optimizers
                _ = sess.run(d_opt,
                             feed_dict={input_z: batch_z,
                                        input_real: batch_images,
                                        lr: learning_rate})
                _ = sess.run(g_opt, 
                             feed_dict={input_z: batch_z, 
                                        input_real: batch_images,
                                        lr: learning_rate})
                                
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    #Print the current image
                    show_generator_output(sess, show_images, input_z, image_channels, data_image_mode)

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 64
z_dim = 100
learning_rate = 0.0002
alpha = 0.05
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode, alpha=alpha)

Epoch 1/2... Discriminator Loss: 1.1132... Generator Loss: 1.6137
Epoch 1/2... Discriminator Loss: 1.1690... Generator Loss: 4.8828
Epoch 1/2... Discriminator Loss: 0.8769... Generator Loss: 3.1823
Epoch 1/2... Discriminator Loss: 2.6030... Generator Loss: 0.4293
Epoch 1/2... Discriminator Loss: 1.0977... Generator Loss: 1.4859

Epoch 1/2... Discriminator Loss: 1.1065... Generator Loss: 1.0271
Epoch 1/2... Discriminator Loss: 1.4620... Generator Loss: 0.5833
Epoch 1/2... Discriminator Loss: 1.8959... Generator Loss: 3.4626
Epoch 1/2... Discriminator Loss: 1.5263... Generator Loss: 0.4998
Epoch 1/2... Discriminator Loss: 1.1891... Generator Loss: 0.8081

Epoch 1/2... Discriminator Loss: 1.1761... Generator Loss: 2.4298
Epoch 1/2... Discriminator Loss: 1.4006... Generator Loss: 0.5730
Epoch 1/2... Discriminator Loss: 0.9876... Generator Loss: 1.3768
Epoch 1/2... Discriminator Loss: 0.9653... Generator Loss: 1.5254
Epoch 1/2... Discriminator Loss: 2.0772... Generator Loss: 0.3485

Epoch 1/2... Discriminator Loss: 1.1093... Generator Loss: 0.8927
Epoch 1/2... Discriminator Loss: 1.0155... Generator Loss: 1.3728
Epoch 1/2... Discriminator Loss: 0.9635... Generator Loss: 1.8748
Epoch 1/2... Discriminator Loss: 1.0794... Generator Loss: 0.8819
Epoch 1/2... Discriminator Loss: 0.8678... Generator Loss: 1.2960

Epoch 1/2... Discriminator Loss: 0.9041... Generator Loss: 2.0210
Epoch 1/2... Discriminator Loss: 0.9352... Generator Loss: 1.4247
Epoch 1/2... Discriminator Loss: 1.1947... Generator Loss: 0.7093
Epoch 1/2... Discriminator Loss: 1.0674... Generator Loss: 1.0089
Epoch 1/2... Discriminator Loss: 0.8930... Generator Loss: 1.1947

Epoch 1/2... Discriminator Loss: 0.8392... Generator Loss: 1.4216
Epoch 1/2... Discriminator Loss: 1.4409... Generator Loss: 0.6287
Epoch 1/2... Discriminator Loss: 1.3737... Generator Loss: 0.5856
Epoch 1/2... Discriminator Loss: 1.1622... Generator Loss: 0.7624
Epoch 1/2... Discriminator Loss: 0.9651... Generator Loss: 2.4587

Epoch 1/2... Discriminator Loss: 0.9172... Generator Loss: 1.3188
Epoch 1/2... Discriminator Loss: 1.0370... Generator Loss: 1.0887
Epoch 1/2... Discriminator Loss: 0.9318... Generator Loss: 1.1455
Epoch 1/2... Discriminator Loss: 0.8594... Generator Loss: 1.3588
Epoch 1/2... Discriminator Loss: 1.1526... Generator Loss: 0.8106

Epoch 1/2... Discriminator Loss: 0.9367... Generator Loss: 1.1772
Epoch 1/2... Discriminator Loss: 1.4493... Generator Loss: 0.5682
Epoch 1/2... Discriminator Loss: 1.5265... Generator Loss: 0.5629
Epoch 1/2... Discriminator Loss: 1.5285... Generator Loss: 0.5215
Epoch 1/2... Discriminator Loss: 1.4553... Generator Loss: 0.5629

Epoch 1/2... Discriminator Loss: 0.9738... Generator Loss: 1.1683
Epoch 1/2... Discriminator Loss: 1.0114... Generator Loss: 1.1464
Epoch 1/2... Discriminator Loss: 0.9472... Generator Loss: 1.9060
Epoch 1/2... Discriminator Loss: 1.1165... Generator Loss: 0.7865
Epoch 1/2... Discriminator Loss: 0.8772... Generator Loss: 1.6080

Epoch 1/2... Discriminator Loss: 0.9493... Generator Loss: 1.4949
Epoch 1/2... Discriminator Loss: 2.0330... Generator Loss: 0.3512
Epoch 1/2... Discriminator Loss: 0.9777... Generator Loss: 1.4860
Epoch 1/2... Discriminator Loss: 1.0359... Generator Loss: 0.9535
Epoch 1/2... Discriminator Loss: 0.9919... Generator Loss: 1.0388

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 64
z_dim = 100
learning_rate = 0.0002
alpha = 0.05
beta1 = 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha=alpha)

Epoch 1/1... Discriminator Loss: 1.5831... Generator Loss: 1.3172
Epoch 1/1... Discriminator Loss: 2.8250... Generator Loss: 0.4540
Epoch 1/1... Discriminator Loss: 3.3451... Generator Loss: 0.3581
Epoch 1/1... Discriminator Loss: 2.4902... Generator Loss: 0.3316
Epoch 1/1... Discriminator Loss: 1.7502... Generator Loss: 0.6957

Epoch 1/1... Discriminator Loss: 1.2208... Generator Loss: 3.3354
Epoch 1/1... Discriminator Loss: 1.1379... Generator Loss: 2.4451
Epoch 1/1... Discriminator Loss: 1.1407... Generator Loss: 1.4246
Epoch 1/1... Discriminator Loss: 1.1969... Generator Loss: 1.6739
Epoch 1/1... Discriminator Loss: 1.5350... Generator Loss: 0.7353

Epoch 1/1... Discriminator Loss: 1.5339... Generator Loss: 3.1790
Epoch 1/1... Discriminator Loss: 1.0773... Generator Loss: 1.3561
Epoch 1/1... Discriminator Loss: 1.5909... Generator Loss: 0.4540
Epoch 1/1... Discriminator Loss: 1.7428... Generator Loss: 0.4210
Epoch 1/1... Discriminator Loss: 1.1977... Generator Loss: 1.0080

Epoch 1/1... Discriminator Loss: 1.4436... Generator Loss: 0.6413
Epoch 1/1... Discriminator Loss: 1.0787... Generator Loss: 2.3420
Epoch 1/1... Discriminator Loss: 1.2707... Generator Loss: 0.8230
Epoch 1/1... Discriminator Loss: 1.4531... Generator Loss: 0.7597
Epoch 1/1... Discriminator Loss: 1.2058... Generator Loss: 0.9013

Epoch 1/1... Discriminator Loss: 1.4449... Generator Loss: 0.6249
Epoch 1/1... Discriminator Loss: 1.8994... Generator Loss: 0.3327
Epoch 1/1... Discriminator Loss: 1.0909... Generator Loss: 0.8715
Epoch 1/1... Discriminator Loss: 1.1909... Generator Loss: 0.9218
Epoch 1/1... Discriminator Loss: 0.9950... Generator Loss: 1.3711

Epoch 1/1... Discriminator Loss: 1.3982... Generator Loss: 2.3108
Epoch 1/1... Discriminator Loss: 1.3147... Generator Loss: 0.7050
Epoch 1/1... Discriminator Loss: 1.4327... Generator Loss: 1.6224
Epoch 1/1... Discriminator Loss: 1.4457... Generator Loss: 0.5679
Epoch 1/1... Discriminator Loss: 1.2329... Generator Loss: 0.8357

Epoch 1/1... Discriminator Loss: 1.2986... Generator Loss: 0.9778
Epoch 1/1... Discriminator Loss: 1.2827... Generator Loss: 1.0129
Epoch 1/1... Discriminator Loss: 1.3309... Generator Loss: 0.8267
Epoch 1/1... Discriminator Loss: 1.7691... Generator Loss: 0.4252
Epoch 1/1... Discriminator Loss: 1.7282... Generator Loss: 0.4089

Epoch 1/1... Discriminator Loss: 1.2734... Generator Loss: 0.8248
Epoch 1/1... Discriminator Loss: 1.4577... Generator Loss: 0.7103
Epoch 1/1... Discriminator Loss: 1.6017... Generator Loss: 0.4985
Epoch 1/1... Discriminator Loss: 1.5246... Generator Loss: 0.4899
Epoch 1/1... Discriminator Loss: 1.4528... Generator Loss: 0.4875

Epoch 1/1... Discriminator Loss: 1.3071... Generator Loss: 0.8833
Epoch 1/1... Discriminator Loss: 1.5438... Generator Loss: 1.6781
Epoch 1/1... Discriminator Loss: 1.5499... Generator Loss: 0.5220
Epoch 1/1... Discriminator Loss: 1.2522... Generator Loss: 0.7862
Epoch 1/1... Discriminator Loss: 2.1436... Generator Loss: 2.5670

Epoch 1/1... Discriminator Loss: 1.2343... Generator Loss: 0.9707
Epoch 1/1... Discriminator Loss: 1.1889... Generator Loss: 0.9588
Epoch 1/1... Discriminator Loss: 1.3584... Generator Loss: 0.7303
Epoch 1/1... Discriminator Loss: 1.2127... Generator Loss: 0.9541
Epoch 1/1... Discriminator Loss: 1.2647... Generator Loss: 1.1807

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.